Publications

Diffusion tractography outside the brain: the road less travelled
Kurt G. Schilling
Irvin Teh
Julien Cohen-Adad
Richard Dortch
Ibrahim Ibrahim
Nian Wang
Bruce Damon
Rory L. Cochran
Alexander Leemans
Diffusion tractography is a powerful MRI technique for mapping fibrous tissue architecture, traditionally applied to the white matter of the… (see more) brain. This report surveys the growing application of tractography to anatomical structures outside the brain, a domain that presents both unique challenges and unique opportunities. We examine its use in the heart, spinal cord, peripheral nerves, brachial plexus, kidney, skeletal muscle, and prostate. For each region, we detail the necessary methodological adaptations for acquisition, modeling, and processing, and highlight the unique anatomical information that can be derived for research and clinical applications. While significant challenges remain - spanning technical hurdles like physiological motion and susceptibility artifacts, to biological complexities like lower anisotropy and the interpretation of streamline validity - tractography beyond the brain provides invaluable, non-invasive insights into tissue micro-organization, opening a new frontier for biomedical imaging.
2026-01-04
Brain Structure & Function (published)
doi.org
Protein Language Models: Is Scaling Necessary?
Quentin Fournier
Robert M. Vernon
Almer Van Der Sloot
Benjamin Schulz
Sarath Chandar
Christopher James Langmead
Public protein sequence databases contain samples from the fitness landscape explored by nature. Protein language models (pLMs) pre-trained … (see more)on these sequences aim to capture this landscape for tasks like property prediction and protein design. Following the same trend as in natural language processing, pLMs have continuously been scaled up. However, the premise that scale leads to better performance assumes that source databases provide an accurate representation of the underlying fitness landscape, which is likely false. By developing an efficient codebase, designing a modern architecture, and addressing data quality concerns such as sample bias, we introduce AMPLIFY, a best-in-class pLM that is orders of magnitude less expensive to train and deploy than previous models. Furthermore, to support the scientific community and democratize the training of pLMs, we have open-sourced AMPLIFY’s pre-training codebase, data, and model checkpoints.
2026-01-04
BioRxiv (preprint)
doi.org
Shaped by meaning, weighted by reliability: New insights into multisensory integration
Elizaveta Sycheva
Léa St-Gelais
Karim Jerbi CoCo Lab
Shahab Bakhtiari
Franco Lepore
Vanessa Hadid
2026-01-04
bioRxiv (preprint)
doi.org
Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning
Jerry Huang
Peng Lu
Qiuhao Zeng
Yusuke Iwasawa
Yutaka Matsuo
A. Chandar
Edison Marrese-Taylor
Irene Li
Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworth… (see more)iness and reliability, yet despite increasing advances in foundation model research, the relationship between such large language models (LLMs) and their calibration remains an open area of research. In this work, we look at a critical gap in the calibration of LLMs within multilingual settings, in an attempt to better understand how the data scarcity can potentially lead to different calibration effects and how commonly used techniques can apply in these settings. Our analysis on two multilingual benchmarks, over 29 and 42 languages respectively, reveals that even in low-resource languages, model confidence can increase significantly after instruction-tuning on high-resource language SFT datasets. However, improvements in accuracy are marginal or non-existent, resulting in mis-calibration, highlighting a critical shortcoming of standard SFT for multilingual languages. Furthermore, we observe that the use of label smoothing to be a reasonable method alleviate this concern, again without any need for low-resource SFT data, maintaining better calibration across all languages. Overall, this highlights the importance of multilingual considerations for both training and tuning LLMs in order to improve their reliability and fairness in downstream use.
2026-01-03
ArXiv (preprint)
doi.org
arxiv.org
Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy
Alice Gravel
Margaret Kalácska
Juan Pablo Arroyo-Mora
Étienne Laliberté
2026-01-03
Canadian Journal of Remote Sensing (published)
doi.org
Carriers of LRRK2 pathogenic variants show a milder, anatomically distinct brain signature of Parkinson's disease
Jakub Kopal
Andrew Vo
Qin Tao
Tanya Simuni
Lana M. Chahine
Danilo Bzdok
Alain Dagher
Pathogenic LRRK2 gene variants are a major genetic risk factor for both familial and sporadic Pa… (see more)rkinson’s dissease (PD), opening an unattended window into disease mechanisms and potential therapies. Investigating the influence of pathogenic variants in LRRK2 gene on brain structure is a crucial step toward enabling early diagnosis and personalized treatment. Yet, despite its significance, the ways in which LRRK2 genotype affects brain structure remain largely unexplored. Work in this domain is plagued by small sample sizes and differences in cohort composition, which can obscure genuine distinctions among clinical subgroups. In this study, we overcome such important limitations by combining explicit modeling of population background variation and pattern matching. Specifically, we leverage a cohort of 603 participants (including 370 with a PD diagnosis) to examine MRI-detectable cortical atrophy patterns associated with the LRRK2 pathogenic variants in people with PD and carriers without Parkinson’s symptoms. LRRK2 PD patients exhibit milder cortical thinning compared to sporadic PD, with notable preservation in temporal and occipital regions, suggesting a distinct pattern of neurodegeneration. Non-manifesting LRRK2 carriers show no significant cortical atrophy, indicating no structural signs of subclinical PD. We further analyze the relationship between aggregated alpha-synuclein in cerebrospinal fluid and atrophy. We find that those with evidence of aggregated alpha-synuclein experienced pronounced neurodegeneration and increased cortical thinning, possibly defining another aggressive PD subtype. Our findings highlight genetic avenues for distinguishing PD subtypes, which could lead to more targeted treatment approaches and a more complete understanding of Parkinson’s disease progression.
2026-01-02
Communications Medicine (published)
doi.org
RetINaBox: A Hands-On Learning Tool for Experimental Neuroscience
Brune Bettler
Flavia Arias Armas
Erica Cianfarano
Vanessa Bordonaro
Megan Q. Liu
Matthew Loukine
Mingyu Wan
Aude Villemain
Blake A. Richards
Stuart Trenholm
An exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdle… (see more)s, the experiment and discovery component of neuroscience is generally lacking in classroom and outreach settings. To address this issue, here we introduce RetINaBox: a low-cost open–source electronic visual system simulator that provides users with a hands-on tool to discover how the visual system builds feature detectors. RetINaBox includes an LED array for generating visual stimuli and photodiodes that act as an array of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors—including ON/OFF, center-surround, orientation-selective, and direction-selective receptive fields—as well as their underlying circuit computations.
2026-01-01
eNeuro (published)
doi.org
Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
Motahareh Sohrabi
Jianxin You
Simon Lacoste-Julien
Eduard Gorbunov
Gauthier Gidel
We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the… (see more) extragradient method rely on two gradient queries per iteration, which limits their analysis and applicability in the online and stochastic settings. We propose a family of Generalized Optimistic Methods with Anchoring (GOMA), which combine two time-scale optimistic updates with an anchoring term inspired by Halpern iteration. In particular, we show that for monotone Lipschitz operators, GOMA achieves an accelerated last-iterate convergence rate of
2025-12-31
International Conference on Machine Learning (Accept (regular))
openreview.net
openreview.net
Active Learning with Non-Uniform Costs for African Natural Language Processing
Bonaventure F. P. Dossou
Ines Arous
Audrey Durand
Jackie Chi Kit Cheung
2025-12-31
Conference of the European Chapter of the Association for Computational Linguistics (published)
doi.org
Additional methodological and statistical details from Testing the scale dependence of plant community assembly processes using imaging spectroscopy
Anna L. Crofts
J. Pablo Arroyo-Mora
Margaret Kalácska
Étienne Laliberté
Mark Vellend
Additional methodological details (including, Figure S1-S2) and detailed statistical results (Figure S3-S4 and Table S1-S3).
2025-12-31
Figshare (unknown)
doi.org
Algorithmic Fairness Across Alignment Procedures and Agentic Systems
Zeyu Tang
Prakhar Ganesh
Awa Dieng
Miriam Rateike
Jamelle Watson-Daniels
Golnoosh Farnadi
Jessica Schrouff
Sanmi Koyejo
AI has transitioned from predictive models to interactive, autonomous agents capable of reasoning, planning, and executing complex goals. As… (see more) the systems increasingly influence social, economic, and scientific decisions, they determine whose interests are represented and whose opportunities are constrained. Ensuring fairness, therefore, is no longer an ethical preference but a practical imperative. As the fairness challenges are fundamentally transformed by advanced AI systems, traditional algorithmic fairness frameworks developed primarily for prediction and/or prediction-based decision-making no longer suffice. This workshop, _Algorithmic Fairness Across Alignment Procedures and Agentic Systems_ (AFAA), emerges at this pivotal moment as a timely forum for rethinking fairness in AI alignment processes and agentic system development. By examining fairness across alignment procedures and agentic systems, this workshop creates a crucial platform for bridging the gap between rapid technical advances in model capabilities and the equally important advances needed in frameworks of algorithmic fairness to govern these powerful systems.
2025-12-31
Workshop Proposals @ International Conference on Learning Representations (published)
openreview.net
openreview.net
An Area-Efficient Routing Solution for Automorphism Ensemble Decoding of Polar Codes
Jiajie Li
Huayi Zhou
Ryan Seah
Marwan Jalaleddine
Warren J. Gross
2025-12-31
IEEE Transactions on Communications (published)
doi.org